The following description provides a summary of information relevant to the present disclosure and is not an admission that any of the information provided or publications referenced herein is prior art to the present disclosure.
Tuberculosis (TB) causes 1.4 million deaths annually and is associated with substantial personal, social, public health, and economic costs, particularly in those individuals co-infected with HIV and other chronic diseases. Proper, accurate, and timely diagnosis of TB is essential to rapidly identify patients for treatment and targeted public health intervention to prevent spread of disease and minimize the emergence of drug resistant strains. Worldwide, most cases of TB are diagnosed using a sputum smear, clinical symptoms, and/or radiographs. There is a clear imperative for improved diagnostics, because the current diagnosis of mycobacterial disease by microscopic stain for acid-fast bacilli (AFB, e.g. Ziehl-Neelsen method) in sputum fails to detect mycobacteria in approximately 50% of cases of TB. This method of diagnosis performs poorly in HIV co-infected individuals and is particularly problematic when an individual is unable to produce a specimen (e.g. an infant unable to produce sputum) or has disease outside of the lung (extrapulmonary). According to the World Health Organization (WHO) estimates, the global case detection rate is just 63%, and only half of the TB cases in Africa are detected and notified (McNerney and Daley (2011) Nat Rev Microbiol 9(3):204-213). The under-diagnosis of TB is critically important in AIDS due to the high mortality associated with TB-HIV co-infection. Diagnosing TB in HIV negative patients with contagious disease is a priority intervention to continue progress in decreasing the worldwide TB incidence. An estimated 400,000 people died of HIV-related TB in 2009, which makes TB responsible for one in four AIDS deaths.
Undiagnosed patients are a major reservoir for spread of disease including drug resistant TB. Microbiological techniques required for specific identification and drug susceptibility can take days to weeks and are often not available in resource poor and remote areas. A rapid, accurate, and inexpensive TB test used by personnel in the clinic or local hospital would add tremendous value to public health in areas with limited resources by identifying those in need of treatment rapidly and hence decrease the spread of disease to others. The lack of a point-of-care (POC) test has been identified as a major gap in the existing pipeline of TB diagnostics (Pal et al. (2010) Curr Opin Pulm Med 16(3):271-284).
Clinical response to treatment for tuberculosis is manifest by improvement in constitutional symptoms, decreased microbial burden, lessening risk of spread to others and fairly rapid return to well-being in patients treated with multiple drug therapy, but predicting who will ultimately relapse requires long-term clinical follow up. With new regimens that may significantly shorten TB treatment duration, more rapid surrogate markers for sterilizing regimens are needed. (Spigelman et al. (2010) The international journal of tuberculosis and lung disease: the official journal of the International Union against Tuberculosis and Lung Disease 14:663-664; Wallis et al. (2009) The Lancet infectious diseases 9:162-172). There is no perfect surrogate endpoint of cure (defined as the absence of relapse after 1 to 2 years of close clinical follow-up); however, the 8-week sputum culture status is the most widely accepted endpoint (Chakera et al. (2011) Biomarkers in medicine 5:131-148; Nemeth et al. (2011) Clinical immunology 138:50-59; Wallis et al. (2009) The Lancet infectious diseases 9:162-172).
The discovery of robust protein biomarkers for treatment response that could be used earlier in treatment assessments is expected to have implications for clinical trials and potentially be helpful for clinical care of patients. Due to the rarity of relapses (<5% of patients relapse in studies of drug susceptible disease using standard therapy) large sample sizes need to be included in clinical trials, typically 75-300 subjects per arm or comparison group. It has been suggested that in TB trials serial measurement of surrogate markers such as multiple serum proteins with large dynamic range could reduce patient sample sizes by 50-90% and decrease the time and monetary investment in desperately needed human trials. (Burman (2003) American journal of respiratory and critical care medicine 167:1299-1301; Nahid et al. (2011) American journal of respiratory and critical care medicine 184 (8):972-979e). Accordingly, a need exists for biomarkers, methods, devices, reagents, systems, and kits that enable the diagnosis, prognosis, treatment response markers and determination of recurrence or prediction of reactivation of TB.
Biomarker selection for a specific disease state involves first the identification of markers that have a measurable and statistically significant difference in a disease population compared to a control population for a specific medical application. Biomarkers can include secreted or shed molecules that parallel disease development or progression and readily diffuse into the blood stream or other body fluids from TB tissue or from surrounding tissues and circulating cells in response to a TB. The biomarker or set of biomarkers identified are generally clinically validated or shown to be a reliable indicator for the original intended use for which it was selected. Biomarkers can include small molecules, peptides, proteins, and nucleic acids. Some of the key issues that affect the identification of biomarkers include over-fitting of the available data and bias in the data.
A variety of methods have been used in an attempt to identify biomarkers for evaluation, diagnosis, prognosis and determination of recurrence or reactivation of disease. For protein-based markers, these include two-dimensional electrophoresis, mass spectrometry, and immunoassay methods. For nucleic acid markers, these include mRNA expression profiles, microRNA profiles, fluorescence in situ hybridization (FISH), serial analysis of gene expression (SAGE), methylation profiles, and large-scale gene expression arrays.
The utility of two-dimensional electrophoresis is limited by low detection sensitivity; issues regarding protein solubility, charge, and hydrophobicity; gel reproducibility; and the possibility of a single spot representing multiple proteins. For mass spectrometry, depending on the format used, limitations revolve around the sample processing and separation, sensitivity to low abundance proteins, signal to noise considerations, and inability to immediately identify the detected protein, lipid or small molecule. Limitations in immunoassay approaches to biomarker discovery are centered on the inability of antibody-based multiplex assays to measure a large number of analytes. One might simply print an array of antibodies and, without sandwiches, measure the analytes bound to those antibodies. This would be the formal equivalent of using a whole genome of nucleic acid sequences to measure by hybridization all DNA or RNA sequences in an organism or a cell. The hybridization experiment works because hybridization can be a stringent test for identity. Even high-affinity antibodies are not stringent enough in selecting their binding partners to work in the context of blood or even cell extracts because the protein ensemble in those matrices have extremely different abundances. Thus, one must use a different approach with immunoassay-based approaches to biomarker discovery—one would need to use multiplexed ELISA assays (that is, sandwiches) to get sufficient stringency to measure many analytes simultaneously to decide which analytes are indeed biomarkers. Sandwich immunoassays do not scale to high content, and thus biomarker discovery using stringent sandwich immunoassays is not possible using standard array formats. Lastly, antibody reagents are subject to substantial lot variability and reagent instability. The instant platform for protein biomarker discovery overcomes these problems.
Many of these methods rely on or require some type of sample fractionation prior to the analysis. Thus the sample preparation required to run a sufficiently powered study designed to identify and discover statistically relevant biomarkers in a series of well-defined sample populations is extremely difficult, costly, and time consuming. During fractionation, a wide range of variability can be introduced into the various samples. For example, a potential marker could be unstable to the process, the concentration of the marker could be changed, inappropriate aggregation or disaggregation could occur, and inadvertent sample ‘contamination could occur and thus obscure the subtle changes anticipated in early disease.
It is widely accepted that biomarker discovery and detection methods using these technologies have serious limitations for the identification of diagnostic biomarkers. These limitations include an inability to detect low-abundance biomarkers, an inability to consistently cover the entire dynamic range of the proteome, irreproducibility in sample processing and fractionation, and overall irreproducibility and lack of robustness of the method. Further, these methods have introduced biases into the data and not adequately addressed the complexity of the sample populations, including appropriate controls, in terms of the distribution and randomization required to identify and validate biomarkers within a target disease population.
Although efforts aimed at the discovery of new and effective TB biomarkers have gone on for several decades, the efforts have been largely unsuccessful. Biomarkers for various diseases typically have been identified in academic laboratories, usually through an accidental discovery while doing basic research on some disease process. Based on the discovery and with small amounts of clinical data, papers were published that suggested the identification of a new biomarker. Most of these proposed biomarkers, however, have not been confirmed as real or useful biomarkers, primarily because the small number of clinical samples tested, and have provided only weak statistical proof that an effective biomarker has in fact been found. That is, the initial identification was not rigorous with respect to the basic elements of statistics. In each of the years 1994 through 2003, a search of the scientific literature shows that thousands of references directed to biomarkers were published. During that same timeframe, however, the FDA approved for diagnostic use, at most, three new protein biomarkers in a year, and in several years, no new protein biomarkers were approved.
Based on the history of failed biomarker discovery efforts, mathematical theories have been proposed that further promote the general understanding that biomarkers for disease are rare and difficult to find. Biomarker research based on 2D gels or mass spectrometry supports these notions. Very few useful biomarkers have been identified through these approaches. However, it is usually overlooked that 2D gel and mass spectrometry measure proteins that are present in blood at approximately 1 nM concentrations and higher, and that this ensemble of proteins may well be the least likely to change with disease. Other than the instant biomarker discovery platform, proteomic biomarker discovery platforms that are able to accurately measure protein expression levels at much lower concentrations do not exist.
Much is known about biochemical pathways for complex human biology. Many biochemical pathways culminate in or are started by secreted proteins that work locally within the pathology, for example, growth factors are secreted to stimulate the replication of other cells in the pathology, and other factors are secreted to ward off the immune system, and so on. While many of these secreted proteins work in a paracrine fashion, some operate distally in the body. One skilled in the art with a basic understanding of biochemical pathways would understand that many pathology-specific proteins ought to exist in blood at concentrations below (even far below) the detection limits of 2D gels and mass spectrometry. What must precede the identification of this relatively abundant number of disease biomarkers is a proteomic platform that can analyze proteins at concentrations below those detectable by 2D gels or mass spectrometry.